Publikationen von Henning Pohl

2017

2016

2015

2014

2013

2012

2011

  • Touch Input on Curved Surfaces
    Henning Pohl, Anne Roudaut and Patrick Baudisch
    Proceedings of the 2011 annual conference on Human factors in computing systems - CHI '11
    Advances in sensing technology are currently bringing touch input to non-planar surfaces, ranging from spherical touch screens to prototypes the size and shape of a ping-pong ball. To help interface designers create usable interfaces on such devices, we determine how touch surface curvature affects targeting. We present a user study in which participants acquired targets on surfaces of different curvature and at locations of different slope. We find that surface convexity increases pointing accuracy, and in particular reduces the offset between the input point perceived by users and the input point sensed by the device. Concave surfaces, in contrast, are subject to larger error offsets. This is likely caused by how concave surfaces hug the user's finger, thus resulting in a larger contact area. The effect of slope on targeting, in contrast, is unexpected at first sight. Some targets located downhill from the user's perspective are subject to error offsets in the opposite direction from all others. This appears to be caused by participants acquiring these targets using a different finger posture that lets them monitor the position of their fingers more effectively.

2010

  • Dance Pattern Recognition using Dynamic Time Warping
    Henning Pohl and Aristotelis Hadjakos
    Proceedings of the 7th Sound and Music Computing Conference (SMC 2010)
    In this paper we describe a method to detect patterns in dance movements. Such patterns can be used in the context of interactive dance systems to allow dancers to influence computational systems with their body movements. For the detection of motion patterns, dynamic time warping is used to compute the distance between two given movements. A custom threshold clustering algorithm is used for subsequent unsupervised classification of movements. For the evaluation of the presented method, a wearable sensor system was built. To quantify the accuracy of the classification, a custom label space mapping was designed to allow comparison of sequences with disparate label sets.